Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models

By Ilya Shpitser, Judea Pearl. 2006

390 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Fair Inference on Outcomes (2017, background): “The reason this does not work in general is not all features are possible to measure, and in general counterfactual probabilities are complex functions of the observed data, not just conditional densities (Shpitser and Pearl 2006).”
Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding (2012, methodology): “Y = α0 + α1 · A + α2 · M + α3 · L + ǫy (5)”
Structural Intervention Distance (SID) for Evaluating Causal Graphs (2013, background): “In the presence of hidden variables, the true structure can be represented by an acyclic directed mixed graph (ADMG), for which Shpitser and Pearl (2006) address the characterization of identifiable intervention distributions.”
Do-calculus when the True Graph Is Unknown (2015, methodology): “, the identification) of causal effects from the passive observational distribution over the variables (Pearl, 2000; Shpitser and Pearl, 2006b).”
Transportability from Multiple Environments with Limited Experiments: Completeness Results (2014, background): “Consider a pair of R-rooted C-forests F = 〈F, F ′〉 such that F ′ ⊂ F , F ′ ∩X = ∅, F ∩X 6= ∅, and R ⊆ An(Y)GX (called a hedge [22]).”
Complete Identification Methods for the Causal Hierarchy (2008, methodology): “This paper consolidates and expands previous results (Shpitser and Pearl, 2006a,b, 2007).”
Transportability from Multiple Environments with Limited Experiments (2013, methodology): “3), and is based on the C-component decomposition for identification of causal effects [22, 23] (and a version of the identification algorithm called ID).”
Causal Transportability with Limited Experiments (2013, methodology): “Some of the previous analyses of identifiability, zidentifiability, and transportability [Kuroki and Miyakawa, 1999; Tian and Pearl, 2002; Shpitser and Pearl, 2006; Huang and Valtorta, 2006; BP, 2012b; 2012a] will be useful in the algorithmization of z-transportability which generalizes these proble”
Meta-Transportability of Causal Effects: A Formal Approach (2013, methodology): “We build on three particular observations articulated along the paper: 2012), which are based on hedges (Shpitser and Pearl, 2006).”
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport (2018, methodology): “Interventional Distributions: We now build up to the Identification (ID) algorithm (Tian and Pearl, 2002; Shpitser and Pearl, 2006b), a sound and complete algorithm (Shpitser and Pearl, 2006b) for determining whether or not an interventional distribution is identifiable, and if so, its form as a…”
CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS (2014, background): “A complete theory for identifying Y(a) from p(V), where A and Y are disjoint, was given in [25, 33].”
Transportability across studies: A formal approach (2011, background): “Moreover, since do -calculus is complete, (Shpitser and Pearl, 2006) every valid equality can be obtained by a ﬁnite application of the three rules of the calculus.”
Transportability of Causal Effects: Completeness Results (2012, methodology): “4) and extends previous analysis and algorithms of identifiability given in [Pearl, 1995; Kuroki and Miyakawa, 1999; Tian and Pearl, 2002; Shpitser and Pearl, 2006b; Huang and Valtorta, 2006].”
The Do-Calculus Revisited (2012, background): “Parallel works by [Tian and Pearl, 2002] and [Shpitser and Pearl, 2006] have led to graphical criteria for verifying the identiﬁability of Q as well as polynomial time algorithms for constructing an estimator of Q .”